Bias refers to a systematic error or deviation in the measurements or estimates from the true value or target. In various contexts, bias can refer to different types of biases. Here are a few common meanings of bias in different domains:
Statistical bias: In statistics, bias refers to the difference between the expected value of an estimator or statistical model and the true value of the parameter being estimated. It indicates whether the estimator is systematically overestimating or underestimating the true value. A biased estimator consistently deviates from the true value in a particular direction.
Cognitive bias: In psychology and decision-making, cognitive biases are systematic patterns of deviation from rationality or objective judgment. These biases occur due to mental shortcuts, heuristics, or inherent limitations in human cognition. Cognitive biases can lead to errors in perception, memory, reasoning, and decision-making. Examples of cognitive biases include confirmation bias, availability bias, and anchoring bias.
Media bias: Media bias refers to the perceived or actual bias in the presentation or coverage of news and information by media outlets. It can involve favoring a particular political, ideological, or cultural perspective in the reporting of events, which may result in the distortion of facts or the omission of certain viewpoints. Media bias can affect public perception and understanding of issues.
Sampling bias: Sampling bias occurs when the selection process of a sample from a population is not random and leads to a non-representative sample. This can result in skewed or distorted conclusions and generalizations about the population. Sampling bias can arise due to factors such as non-random selection, self-selection, or non-response bias.
Bias in machine learning: In machine learning, bias refers to the systematic error or inaccuracy in the predictions or decisions made by a model. Bias can occur when the model’s assumptions or structure inadequately capture the underlying patterns in the data, leading to consistent underfitting or overfitting. Addressing bias is important to ensure fair and accurate predictions.
It’s essential to be aware of biases in various contexts to ensure sound decision-making, unbiased analysis, and fair treatment of individuals or groups. Recognizing and mitigating biases is crucial for achieving objectivity, accuracy, and fairness in research, analysis, and decision-making processes.
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